How to Implement an Efficient Quality Control Program in Preclinical Imaging: Insights from the Field
Preclinical studies using laboratory animal models are crucial to understanding the underlying disease mechanism through observations of responses to interventions and physiological and environmental changes at tissue, cell, or molecular level. As for clinical imaging, reliability, reproducibility, and repeatability are essential when groups of animals are used in a longitudinal imaging experiment. The more significant the variability of the imaging endpoint, the more animals are needed to be able to observe statistically significant differences between groups. Therefore, preclinical imaging requires quality control procedures to maintain the reliability, reproducibility, and repeatability of imaging procedures and to ensure the accuracy and precision of SPECT and PET quantification.
Recently, a joint EANM-ESMI procedure guideline has been published to provide recommendations for the implementation of an effective and efficient quality control (QC) programme for SPECT and PET systems in a preclinical imaging lab. These recommendations aim to strengthen the translational power of preclinical imaging results obtained using preclinical SPECT and PET.
In this webinar, one of the authors will provide more insights into the published guidelines. The various criteria for the quality control program covered in these guidelines will be discussed further. Afterward, this webinar will cover how quality control procedures are implemented at MOLECUBES and how we are doing Quality control in our CUBEFLOW.
December 4, 2024 | 04:00 PM CET
Who Should Attend
The webinar is relevant to those engaged in or interested in quality control in preclinical imaging, such as laboratory technicians, researchers, quality control analysts, or specialists. In addition, this webinar will be of interest to multiple profiles in the biomedical research community, especially those applying medical imaging techniques in their work.
Presenter: Prof. Dr. Christian Vanhove (Principal investigator - Institute Biomedical Technology (IBiTech), Associate professor (Faculty of Engineering and Architecture, UGent), Head of Innovative Flemish In-vivo Imaging Technology (INFINITY) lab, Senior staff member Medical Imaging and Signal Processing (MEDISIP) research group)
Christian Vanhove graduated as a Biomedical and Clinical Engineer from Brussels University in 1990. From 1991 until 1996, he worked as a Medical Physicist at the Nuclear Medicine department of the Sint-Elisabeth hospital in Zottegem, where he was doing research and development for the industry in a clinical environment. Research and developments were focused on all aspects of medical image processing, including image reconstruction, image registration and image quantification. In 1996, he moved to the Nuclear Medicine department of the Brussels University Hospital. As a Medical Physicist Expert, he continued his research in the field of medical image processing and obtained his PhD in Medical Science in 2004. In 2005, he was one of the initiators of the laboratory animal imaging lab of Brussels University and worked as a postdoctoral researcher at this preclinical imaging facility. During this period his research was focused on quantitative laboratory animal imaging. Since February 2011, he joined the Institute Biomedical Technology (IBiTech) of the Faculty of Engineering and Architecture of Ghent University, where he is responsible for the INnovative Flemish IN-vivo Imaging TechnologY (INFINITY) lab. INFINITY is part of CORE ARTH and an expertise center with a focus on non-invasive in vivo imaging of laboratory animals. One of the major research domains of INFINITY is multi-modality imaging in cancer research.
Presenter: Dr. Milan Decuyper (R&D Engineer, MOLECUBES)
Milan graduated from Ghent University with a Master of Science in Electrical Engineering, Communication, and Information Technology in 2017. After graduating, he joined the Medical Imaging and Signal Processing (MEDISIP) research group as a PhD researcher. During his PhD, Milan performed research on the use of artificial intelligence in medical imaging. He specifically investigated two applications: neural networks to improve spatial resolution of monolithic PET detectors and MR image analysis for computer-aided brain tumor diagnosis. In 2021, he joined MOLECUBES as an R&D engineer part of the software team, where he currently develops data processing algorithms to bring new features and improvements to our cubes. This involves both optimizing raw measurement data processing and advancing image post-processing and analysis.
